import pandas_datareader as pdr
import yfinance as yf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
yf.pdr_override()

# Using code from FRED API: Get US Economic Data using Python


def get_fred_data(param_list, start_date, end_date):
    df = pdr.DataReader(param_list, "fred", start_date, end_date)
    return df.reset_index()

#pip install fredapi

from fredapi import Fred

fred = Fred(api_key='7490f681c7eb037113b1a75963b074db')

# Delinquency Rate on Credit Card Loans, All Commercial Banks
delinquency_rates = fred.get_series('DRCCLACBS')

import pandas as pd
df_delinquency = pd.DataFrame(delinquency_rates)

import matplotlib.pyplot as plt

# Plotting the delinquency rates
plt.plot(df_delinquency.index, df_delinquency[0])

# Adding labels and title
plt.xlabel('Date')
plt.ylabel('Delinquency Rate')
plt.title('Delinquency Rate on Credit Card Loans')

# Display the plot
plt.show()

# Commercial Bank Interest Rate on Credit Card Plans, All Accounts
creditCard_interest_rates = fred.get_series('TERMCBCCALLNS')

df_interest = pd.DataFrame(creditCard_interest_rates)

df_interest.info() # There are 226 null values. Scatter plot will be more suitable for this data

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 343 entries, 1994-11-01 to 2023-05-01
Data columns (total 1 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   0       115 non-null    float64
dtypes: float64(1)
memory usage: 5.4 KB

# Create the line plot
plt.scatter(df_interest.index, df_interest[0])

# Set the x-axis label, y-axis label, and title
plt.xlabel('Date')
plt.ylabel('Interest Rate')
plt.title('Commercial Bank Interest Rates on Credit Card Plans')

# Show the plot
plt.show()

# Large Bank Consumer Credit Card Balances: Utilization: Active Accounts Only: 90th Percentile
credit_utilization_rate = fred.get_series('RCCCBACTIVEUTILPCT90')

df_utilization = pd.DataFrame(credit_utilization_rate)

import matplotlib.pyplot as plt

# Line plot
plt.plot(df_utilization.index, df_utilization[0])
plt.xlabel('Date')
plt.ylabel('Utilization Rate')
plt.title('Credit Card Utilization Rate over Time')
plt.show()

# U.S.-Chartered Depository Institutions; Consumer Credit, Credit Cards; Asset, Transactions
depository_institutions_creditcard_transactions = fred.get_series('BOGZ1FA763066113Q')

df_depository_credit_card_trans = pd.DataFrame(depository_institutions_creditcard_transactions)

df_depository_credit_card_trans.dropna(inplace=True)

df_depository_credit_card_trans = df_depository_credit_card_trans[df_depository_credit_card_trans[0]>0]

# Plotting the line chart
plt.figure(figsize=(10, 6))
plt.plot(df_depository_credit_card_trans.index, df_depository_credit_card_trans[0])

# Customizing the chart
plt.title('Credit Card Transactions by U.S. Chartered Depository Institutions')
plt.xlabel('Date')
plt.ylabel('Transaction Amount')
plt.xticks(rotation=45)

# Displaying the chart
plt.show()

# 30-Year Fixed Rate Conforming Mortgage Index: Loan-to-Value Greater Than 80, FICO Score Between 720 and 739
mortgage_index_720_739 = fred.get_series('OBMMIC30YFLVGT80FB720A739')
df_mortgage_index_720_739 = pd.DataFrame(mortgage_index_720_739)

# 30-Year Fixed Rate Conforming Mortgage Index: Loan-to-Value Less Than or Equal to 80, FICO Score Greater Than 740
mortgage_index_740 = fred.get_series('OBMMIC30YFLVLE80FGE740')
df_mortgage_index_740 = pd.DataFrame(mortgage_index_740)

import matplotlib.pyplot as plt

# Plotting the first mortgage index series
plt.plot(df_mortgage_index_720_739.index, df_mortgage_index_720_739[0], label='Loan-to-Value > 80, FICO 720-739')

# Plotting the second mortgage index series
plt.plot(df_mortgage_index_740.index, df_mortgage_index_740[0], label='Loan-to-Value <= 80, FICO > 740')

# Adding labels and title
plt.xlabel('Date')
plt.ylabel('Mortgage Index')
plt.title('Comparison of Mortgage Index Series')
plt.legend()

# Displaying the plot
plt.show()

unemployment_rate = fred.get_series('UNRATE')
gdp = fred.get_series('GDP')
inflation_rate = fred.get_series('CPIAUCSL')
interest_rate = fred.get_series('FEDFUNDS')
labor_force_participation = fred.get_series('CIVPART')
consumer_sentiment = fred.get_series('UMCSENT')
financial_index = fred.get_series('SP500')

unemployment_rate

1948-01-01    3.4
1948-02-01    3.8
1948-03-01    4.0
1948-04-01    3.9
1948-05-01    3.5
             ... 
2023-04-01    3.4
2023-05-01    3.7
2023-06-01    3.6
2023-07-01    3.5
2023-08-01    3.8
Length: 908, dtype: float64

info = fred.get_series_info('UNRATE')

info

id                                                                      UNRATE
realtime_start                                                      2023-09-28
realtime_end                                                        2023-09-28
title                                                        Unemployment Rate
observation_start                                                   1948-01-01
observation_end                                                     2023-08-01
frequency                                                              Monthly
frequency_short                                                              M
units                                                                  Percent
units_short                                                                  %
seasonal_adjustment                                        Seasonally Adjusted
seasonal_adjustment_short                                                   SA
last_updated                                            2023-09-01 07:45:02-05
popularity                                                                  94
notes                        The unemployment rate represents the number of...
dtype: object

import matplotlib.pyplot as plt

# Unemployment Rate
plt.figure(figsize=(8, 4))
plt.plot(unemployment_rate)
plt.title("Unemployment Rate")
plt.xlabel("Date")
plt.ylabel("Rate")
plt.show()

# GDP
plt.figure(figsize=(8, 4))
plt.plot(gdp)
plt.title("Gross Domestic Product (GDP)")
plt.xlabel("Date")
plt.ylabel("Value")
plt.show()

# Inflation Rate
plt.figure(figsize=(8, 4))
plt.plot(inflation_rate)
plt.title("Inflation Rate")
plt.xlabel("Date")
plt.ylabel("Rate")
plt.show()

# Interest Rate
plt.figure(figsize=(8, 4))
plt.plot(interest_rate)
plt.title("Interest Rate")
plt.xlabel("Date")
plt.ylabel("Rate")
plt.show()

# Labor Force Participation
plt.figure(figsize=(8, 4))
plt.plot(labor_force_participation)
plt.title("Labor Force Participation")
plt.xlabel("Date")
plt.ylabel("Rate")
plt.show()

# Consumer Sentiment
plt.figure(figsize=(8, 4))
plt.plot(consumer_sentiment)
plt.title("Consumer Sentiment")
plt.xlabel("Date")
plt.ylabel("Index")
plt.show()

# Financial Index
plt.figure(figsize=(8, 4))
plt.plot(financial_index)
plt.title("Financial Index (S&P 500)")
plt.xlabel("Date")
plt.ylabel("Value")
plt.show()

estimated_market_value_of_owned_home = fred.get_series('CXU800721LB1210M')
existing_home_sales = fred.get_series('EXHOSLUSM495S')
house_price_index = fred.get_series('USSTHPI')
house_price_index_california = fred.get_series('CASTHPI')

import matplotlib.pyplot as plt

# Plotting Estimated Market Value of Owned Home
plt.figure(figsize=(10, 6))
plt.plot(estimated_market_value_of_owned_home, color='blue')
plt.title('Estimated Market Value of Owned Home')
plt.xlabel('Date')
plt.ylabel('Market Value')
plt.grid(True)
plt.show()

# Plotting Existing Home Sales
plt.figure(figsize=(10, 6))
plt.plot(existing_home_sales, color='red')
plt.title('Existing Home Sales')
plt.xlabel('Date')
plt.ylabel('Number of Sales')
plt.grid(True)
plt.show()

# Plotting House Price Index
plt.figure(figsize=(10, 6))
plt.plot(house_price_index, color='green')
plt.title('House Price Index')
plt.xlabel('Date')
plt.ylabel('Index Value')
plt.grid(True)
plt.show()

# Plotting House Price Index (California)
plt.figure(figsize=(10, 6))
plt.plot(house_price_index_california, color='purple')
plt.title('House Price Index (California)')
plt.xlabel('Date')
plt.ylabel('Index Value')
plt.grid(True)
plt.show()

data1 = pd.read_csv('MPCT03XXS.csv')

ata1 = pd.read_csv("MPCT03XXS.csv")

def value_plot(data, y, title, figscale=1):
    plt.figure(figsize=(8 * figscale, 4 * figscale))
    data[y].plot(kind='line', title=title)
    plt.gca().spines[['top', 'right']].set_visible(False)
    plt.tight_layout()
    plt.show()  # Display the plot

value_plot(data1, 'value', title='Analysis of Value Trends Over Time')

def histogram(data, colname, num_bins=20, figscale=1):
    data[colname].plot(kind='hist', bins=num_bins, title=colname, figsize=(8*figscale, 4*figscale))
    plt.gca().spines[['top', 'right']].set_visible(False)
    plt.tight_layout()
    plt.show()

data1 = pd.read_csv('MPCT03XXS.csv')

histogram(data1, 'value')

import os
import csv
import nltk, re
import requests
import numpy as np
import pandas as pd
import hvplot.pandas
import seaborn as sns
import yfinance as yf
import dataframe_image as dfi
from wordcloud import WordCloud
import pandas_datareader as pdr
import matplotlib.pyplot as plt
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from pandas_datareader.data import DataReader
from sklearn.preprocessing import MinMaxScaler
from nltk.sentiment.vader import SentimentIntensityAnalyzer

from datetime import timedelta, datetime
import datetime as dt

yf.pdr_override()
sns.set_style('whitegrid')
plt.style.use("fivethirtyeight")
%matplotlib inline

import fredpy as fp
fp.api_key = '7490f681c7eb037113b1a75963b074db'

from fredapi import Fred
fred = Fred(api_key='7490f681c7eb037113b1a75963b074db')

api_key = 'O2ZMKAVEVDVQ4RWF'

# Base Alpha Vantage endpoint & parameters
url = "https://www.alphavantage.co/query"
params = {"symbol": "AAPL" ,"apikey": api_key}

# Fetch Annual Earnings per share, AEPS data
params["function"] = "EARNINGS"
response = requests.get(url, params=params)
annual_eps_data = response.json()['annualEarnings']
eps_df = pd.DataFrame(annual_eps_data)
eps_df =eps_df.iloc[:10] 

# Fetch Adjusted Income Statement
params["function"] = "INCOME_STATEMENT"
response = requests.get(url, params=params)
income_data = response.json()['annualReports'] 
income_df = pd.DataFrame(income_data)
income_df =income_df.iloc[:,:5] 

# Fetch Balance Sheet
params["function"] = "BALANCE_SHEET"
response = requests.get(url, params=params)
balance_data = response.json()['annualReports']
balance_df = pd.DataFrame(balance_data)
balance_df = balance_df.iloc[:,:5] 

# Fetch Cash Flow
params["function"] = "CASH_FLOW"
response = requests.get(url, params=params)
cash_flow_data = response.json()['annualReports']
cash_df = pd.DataFrame(cash_flow_data)
cash_df = cash_df.iloc[:,:5] 

eps_df = eps_df.style.set_caption("AAPL Annual Earnings Per Share")  
income_df = income_df.style.set_caption("AAPL Adjusted and Truncated Income Statement")  
balance_df = balance_df.style.set_caption("AAPL Truncated Balance Sheet")  
cash_df = cash_df.style.set_caption("AAPL Truncated Cash Flow")

eps_df

balance_df

cash_df

income_df

# The tech stocks we'll use for this analysis
tech_list = ['AAPL', 'GOOG', 'MSFT', 'AMZN']

# Set up End and Start times for data grab
tech_list = ['AAPL', 'GOOG', 'MSFT', 'AMZN']

end = datetime.now()
start = datetime(end.year - 1, end.month, end.day)

for stock in tech_list:
    globals()[stock] = yf.download(stock, start, end ,progress=False)
    

company_list = [AAPL, GOOG, MSFT, AMZN]
company_name = ["APPLE", "GOOGLE", "MICROSOFT", "AMAZON"]

for company, com_name in zip(company_list, company_name):
    company["company_name"] = com_name
    
df = pd.concat(company_list, axis=0)
df.tail(10)

# Summary Stats
AAPL.describe()

# General info
AAPL.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 251 entries, 2022-09-28 to 2023-09-27
Data columns (total 7 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   Open          251 non-null    float64
 1   High          251 non-null    float64
 2   Low           251 non-null    float64
 3   Close         251 non-null    float64
 4   Adj Close     251 non-null    float64
 5   Volume        251 non-null    int64  
 6   company_name  251 non-null    object 
dtypes: float64(5), int64(1), object(1)
memory usage: 15.7+ KB

# Let's see a historical view of the closing price
plt.figure(figsize=(15, 10))
plt.subplots_adjust(top=1.25, bottom=1.2)

for i, company in enumerate(company_list, 1):
    plt.subplot(2, 2, i)
    company['Adj Close'].plot()
    plt.ylabel('Adj Close')
    plt.xlabel(None)
    plt.title(f"Closing Price of {tech_list[i - 1]}")
    
plt.tight_layout()

plt.show()

from pandas_datareader import data as pdrdata
# # Grab all the closing prices for the tech stock list into one DataFrame
closing_df = pdrdata.get_data_yahoo(tech_list, start=start, end=end ,progress=False )['Adj Close']

# Set up our figure by naming it returns_fig, call PairPLot on the DataFrame
returns_fig = sns.PairGrid(closing_df)

# Using map_upper we can specify what the upper triangle will look like.
returns_fig.map_upper(plt.scatter,color='purple')

# We can also define the lower triangle in the figure, inclufing the plot type (kde) or the color map (BluePurple)
returns_fig.map_lower(sns.kdeplot,cmap='cool_d')

# Finally we'll define the diagonal as a series of histogram plots of the daily return
returns_fig.map_diag(plt.hist,bins=30)

<seaborn.axisgrid.PairGrid at 0x2134513ff90>

# # Make a new tech returns DataFrame
tech_rets = closing_df.pct_change()

plt.figure(figsize=(10,9))
plt.tight_layout()

plt.subplot(1, 2, 1)
sns.heatmap(tech_rets.corr(), annot=True, cmap='summer')
plt.title('Correlation of stock return')

plt.subplot(1, 2, 2)
sns.heatmap(closing_df.corr(), annot=True, cmap='summer')
plt.title('Correlation of stock closing price')
plt.show()

rets = tech_rets.dropna()

area = np.pi * 20

plt.figure(figsize=(10, 8))
plt.scatter(rets.mean(), rets.std(), s=area)
plt.xlabel('Expected return')
plt.ylabel('Risk')

for label, x, y in zip(rets.columns, rets.mean(), rets.std()):
    plt.annotate(label, xy=(x, y), xytext=(50, 50), textcoords='offset points', ha='right', va='bottom', 
                 arrowprops=dict(arrowstyle='-', color='blue', connectionstyle='arc3,rad=-0.3'))
plt.show()

def growth_rate(value,df):
    
    global initial_value
    if df[0] == value:
        initial_value = value
        return 0
    
    else:
        rate = f'{((value - initial_value) /initial_value) :.2f}'
        initial_value = value
        return float(rate) * 100

def series_to_dataframe(series_object, column_name):

    df = series_object.to_frame(name=column_name)
    df.reset_index(inplace=True)
    df.rename(columns={'index':'date'},inplace=True)

    df['date'] = pd.to_datetime(df['date'])
    df['month'] = df['date'].dt.month
    df['year'] = df['date'].dt.year 
    
    new_df= df.loc[df['month']==10].copy()
    new_df = new_df.loc[df['year']>=1948].copy()
    new_df.reset_index(drop=True,inplace=True)
    new_df.drop(['date','month'],axis=1,inplace=True)

    return new_df

def plot_many(number,*plots,labels,colors,title=None):

    plt.figure(figsize=(10,3))
    for plotno in range(number):
        X=plots[0][plotno][0]
        Y=plots[0][plotno][1]
        
        plt.plot(X,Y,colors[plotno],label=labels[plotno])
    
    if title:
        plt.title(title)
    plt.legend(loc='upper left')
    

    plt.show()

#retrieving the GDP figures

gdp = fred.get_series('GDPC1') #quarterly data
gdp.dropna(axis=0,inplace=True)
gdp_df = gdp.to_frame(name='gdp')
gdp_df['quarter'] = gdp_df.index
gdp_df.index = [index for index in  range(len(gdp_df))]


gdp_df['gdp_growth'] = gdp_df['gdp'].apply(growth_rate, df=gdp_df['gdp'])
gdp_df['quarter'] = pd.to_datetime(gdp_df['quarter'])
gdp_df['month'] = gdp_df['quarter'].dt.month

#gdp growth rate per year
gdp_year_df=gdp_df.loc[gdp_df['month']==10].copy()
gdp_year_df['year'] = gdp_year_df['quarter'].dt.year
gdp_year_df.reset_index(drop=True, inplace=True)
gdp_year_df.drop('month',axis=1,inplace=True)
gdp_year_df.drop('quarter',axis=1,inplace=True)

gdp_year_df['gdp_growth'] = gdp_year_df['gdp'].apply(growth_rate,df=gdp_year_df['gdp'])

cpi = fred.get_series('CORESTICKM159SFRBATL')

cpi_df = series_to_dataframe(cpi,column_name='rate')
plot_many(2,[[cpi_df['year'],cpi_df['rate']],
             [gdp_year_df['year'],gdp_year_df['gdp_growth']]],
             labels=['Inflation','Gdp Growth'],
             colors=['red','blue'])

def series_to_dataframe(series_object, column_name):

    df = series_object.to_frame(name=column_name)
    df.reset_index(inplace=True)
    df.rename(columns={'index':'date'},inplace=True)

    df['date'] = pd.to_datetime(df['date'])
    df['month'] = df['date'].dt.month
    df['year'] = df['date'].dt.year 
    
    new_df= df.loc[df['month']==10].copy()
    new_df = new_df.loc[df['year']>=1948].copy()
    new_df.reset_index(drop=True,inplace=True)
    new_df.drop(['date','month'],axis=1,inplace=True)

    return new_df

##Unemployment figures
unef = fred.get_series('UNRATE')
unef_df = series_to_dataframe(unef,column_name='rate')

plt.figure(figsize=(10,3))
plt.plot(unef_df['year'],unef_df['rate'],'r',label='Unemployment Rate')
plt.plot(gdp_year_df['year'],gdp_year_df['gdp_growth'],'g',label='GDP Growth Rate')
plt.legend(loc='upper left')

plt.show()

price_corr_df = pd.read_excel('price_corr_df1.xlsx') 
symbol = 'AAPL'

def create_output_dirs():
    os.makedirs(data_path, exist_ok=True)
    os.makedirs(results_path, exist_ok=True)

def fetch_price_data(symbol):
    #  valid periods: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max
    period = '2y'
    #  valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo
    interval = '1d'
    data = yf.download(tickers=symbol, period=period, interval=interval, progress=False)
    price_df = pd.DataFrame(data)
    price_df.reset_index(inplace=True)
    price_df.to_excel('price_df1.xlsx')
    return price_df

def fetch_sentiment(symbol, start_date_str, end_date_str):
    sentiment_file_name = f"{symbol}_tweet_sentiment_{start_date_str}_{end_date_str}.csv"
    sentiment_file_path = os.path.join(data_path, sentiment_file_name)

    #  Load news file from disk if available
    if os.path.exists(sentiment_file_path):
        news_df = pd.read_csv(sentiment_file_path)
        news_df['date'] = pd.to_datetime(news_df['date'])
        return news_df

    #  Fetch news from EOD Data
    news_base_url = "https://eodhistoricaldata.com/api/sentiments"
    request_url = f"{news_base_url}?api_token={news_api_token}&s={symbol}&from={start_date_str}&to={end_date_str}"
    headers = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:101.0) Gecko/20100101 Firefox/101.0',
        'Accept': 'application/json',
        'Accept-Language': 'en-US,en;q=0.5',
    }

    try:
        #  Send GET request
        response = requests.get(request_url, headers=headers)
        response_obj = response.json()
        data_array = response_obj[f"{symbol}.US"]
        #  Format: {"AAPL.US":[{"date":"2022-10-02","count":4,"normalized":0.302},{"date":"2022-10-01","count":8,"normalized":0.617}]}
        sentiment_df = pd.DataFrame({}, columns=['date', 'count', 'normalized'])
        for item in data_array:
            row_df = pd.DataFrame({'date': [item['date']], 'count': [item['count']], 'normalized': [item['normalized']]})
            sentiment_df = pd.concat([sentiment_df, row_df], axis=0, ignore_index=True)

        sentiment_df.to_csv(sentiment_file_path)
        sentiment_df['date'] = pd.to_datetime(sentiment_df['date'])
        sentiment_df.to_excel('sentiment_df1.xlsx')
        return sentiment_df
    
    except:
        print('Error loading sentiments')
        return None

def fetch_and_process_sentiment(price_df, symbol):
    start_date = price_df.iloc[0]['Date']
    start_date_str = start_date.strftime("%Y-%m-%d")
    today = dt.datetime.now()
    end_date_str = today.strftime("%Y-%m-%d")

    #  Get Tweet sentiment
    sentiment_df = fetch_sentiment(symbol, start_date_str, end_date_str)

    # Iterate through price date and add tweet sentiment
    for i, row in price_df.iterrows():
        current_date = row['Date']

        sentiment_series = sentiment_df[sentiment_df['date'] == current_date]['normalized']
        if sentiment_series is not None and not sentiment_series.empty:
            price_df.at[i, 'sentiment'] = sentiment_series.values[0]
    price_df.to_excel('price_df2.xlsx')
    return price_df

def plot_results(symbol, price_corr_df):
    light_palette = {}
    light_palette["bg_color"] = "#ffffff"
    light_palette["plot_bg_color"] = "#ffffff"
    light_palette["grid_color"] = "#e6e6e6"
    light_palette["text_color"] = "#2e2e2e"
    light_palette["border_color"] = "#2e2e2e"
    light_palette["color_1"] = "#5c285b"
    light_palette["color_5"] = "#de4f51"
    palette = light_palette

    #  Create sub plots
    fig = make_subplots(rows=1, cols=1, subplot_titles=[f"{symbol} Price/Tweet Correlation"], \
                        specs=[[{ "secondary_y": True}]])

    #  Plot close price
    fig.add_trace(go.Scatter(x=price_corr_df.index, y=price_corr_df['close_scaled']
                             , line=dict(color=palette['color_1'], width=1)
                             , name="Close"),             row=1, col=1)

    #  Add Correlation
    fig.add_trace(go.Scatter(x=price_corr_df.index, y=price_corr_df['sentiment_scaled']
                             , line=dict(color=palette['color_5'], width=1)
                             , name="Sentiment"),        row=1, col=1)

    #  Moving average of corr
    price_corr_df['sentiment_scaled_avg'] = price_corr_df['sentiment_scaled'].rolling(10).mean()
    fig.add_trace(go.Scatter(x=price_corr_df.index, y=price_corr_df['sentiment_scaled_avg']
                             , line=dict(color="blue", width=1)
                             , name="Sentiment Avg"),   row=1, col=1)

    fig.update_layout(
            title={'text': '', 'x': 0.5},
            font=dict(family="Verdana", size=12, color=palette["text_color"]),
            autosize=True,
            width=800, height=600,
            xaxis={"rangeslider": {"visible": False}},
            plot_bgcolor=palette["plot_bg_color"],
            paper_bgcolor=palette["bg_color"])
    fig.update_yaxes(visible=False, secondary_y=True)

    #  Change grid color
    fig.update_xaxes(showline=True, linewidth=1, linecolor=palette["grid_color"]
                     ,gridcolor=palette["grid_color"])
    fig.update_yaxes(showline=True, linewidth=1, linecolor=palette["grid_color"]
                     ,gridcolor=palette["grid_color"])

    fig.show()

plot_results(symbol, price_corr_df)

def plot_charts(combined_yield_df):
    month = combined_yield_df.index
    bond =combined_yield_df.DGS2
    fedfund = combined_yield_df.FEDFUNDS

    fig = go.Figure()
    fig.add_trace(go.Scatter(x=month, y=bond, name='2 years bond',
                             line=dict(color='firebrick', width=4)))
    fig.add_trace(go.Scatter(x=month, y=fedfund, name = 'Fed Fund Rates',
                             line=dict(color='royalblue', width=4)))

    fig.update_layout(title='2 Years Bond & Fed Fund Rates',
                       xaxis_title='Month',
                       yaxis_title='Rates')


    fig.show()

if __name__ == '__main__':
    #  Set start date
    today_date = datetime.utcnow()
    start_date = today_date - timedelta(days=5 * 365)
    start_date_str = datetime.strftime(start_date, "%Y-%m-%d")

    #  Federal Reserve Economic Data Service
    data_source = 'fred'
    two_year_treasury_code = 'FEDFUNDS'
    ten_year_treasury_code = 'DGS2'

    #  Fetch data
    two_year_yield_df = pdr.DataReader(two_year_treasury_code, data_source, start_date)
    ten_year_df = pdr.DataReader(ten_year_treasury_code, data_source, start_date)
    
    #  Merge the two yield dfs
    combined_yield_df = pd.merge(two_year_yield_df, ten_year_df, how='inner'
                                 , left_index=True, right_index=True)
    combined_yield_df.dropna(inplace=True)

    #  Plot charts
    plot_charts(combined_yield_df)

def plot_charts(combined_yield_df):
    light_palette = {}
    light_palette["bg_color"] = "#ffffff"
    light_palette["plot_bg_color"] = "#ffffff"
    light_palette["grid_color"] = "#e6e6e6"
    light_palette["text_color"] = "#2e2e2e"
    light_palette["dark_candle"] = "#4d98c4"
    light_palette["light_candle"] = "#b1b7ba"
    light_palette["volume_color"] = "#c74e96"
    light_palette["border_color"] = "#2e2e2e"
    light_palette["color_1"] = "#5c285b"
    light_palette["color_2"] = "#802c62"
    light_palette["color_3"] = "#a33262"
    light_palette["color_4"] = "#c43d5c"
    light_palette["color_5"] = "#de4f51"
    light_palette["color_6"] = "#f26841"
    light_palette["color_7"] = "#fd862b"
    light_palette["color_8"] = "#ffa600"
    light_palette["color_9"] = "#3366d6"
    palette = light_palette

    #  Create sub plots
    fig = make_subplots(rows=2, cols=1, subplot_titles=[f"2-year vs 10-year Yields"
                                                        , 'Yield Difference'], \
                        specs=[[{"secondary_y": True}], [{"secondary_y": True}]], \
                        vertical_spacing=0.1, shared_xaxes=True)

    #  Plot yields
    fig.add_trace(go.Scatter(x=combined_yield_df.index, y=combined_yield_df['DGS2']
                             , line=dict(color=palette['color_5']
                        , width=1), name="2-year yield"), row=1, col=1)
    fig.add_trace(go.Scatter(x=combined_yield_df.index, y=combined_yield_df['DGS10']
                             , line=dict(color=palette['color_9']
                                         , width=1), name="10-year yield"), row=1, col=1)

    # Plot yield curve
    fig.add_trace(go.Scatter(x=combined_yield_df.index, y=combined_yield_df['yield_diff']
                             , line=dict(color=palette['color_1'], width=1), name="Yield diff")
                             , row=2, col=1)

    fig.add_hline(y=0, line=dict(color='red', width=1), row=2, col=1)

    fig.update_layout(
            title={'text': '', 'x': 0.5},
            font=dict(family="Verdana", size=12, color=palette["text_color"]),
            autosize=True,
            width=800, height=600,
            xaxis={"rangeslider": {"visible": False}},
            plot_bgcolor=palette["plot_bg_color"],
            paper_bgcolor=palette["bg_color"])
    fig.update_layout(yaxis2=dict(range=[230, 300]))

    fig.update_xaxes(
        dtick="M1",
        tickformat="%b\n%Y",
        ticklabelmode="period")

    #  Change grid color
    fig.update_xaxes(showline=True, linewidth=1, linecolor=palette["grid_color"], gridcolor=palette["grid_color"])
    fig.update_yaxes(showline=True, linewidth=1, linecolor=palette["grid_color"], gridcolor=palette["grid_color"])


    fig.show()

if __name__ == '__main__':
    #  Set start date
    today_date = datetime.utcnow()
    start_date = today_date - timedelta(days=5 * 365)
    start_date_str = datetime.strftime(start_date, "%Y-%m-%d")

    #  Federal Reserve Economic Data Service
    data_source = 'fred'
    two_year_treasury_code = 'DGS2'
    ten_year_treasury_code = 'DGS10'

    #  Fetch data
    two_year_yield_df = pdr.DataReader(two_year_treasury_code, data_source, start_date)
    ten_year_df = pdr.DataReader(ten_year_treasury_code, data_source, start_date)
    
    #  Merge the two yield dfs
    combined_yield_df = pd.merge(two_year_yield_df, ten_year_df, how='inner'
                                ,left_index=True,right_index=True)
    combined_yield_df.dropna(inplace=True)

    #  Calculate yield difference
    combined_yield_df['yield_diff'] = combined_yield_df['DGS10'] - combined_yield_df['DGS2']

    #  Plot charts
    plot_charts(combined_yield_df)

import datetime as dt
def plot_charts(treasury_yield_df, unemployment_rate_df \
                , savings_df, cpi_df ,ppi_df):
    light_palette = {}
    light_palette["bg_color"] = "#ffffff"
    light_palette["plot_bg_color"] = "#ffffff"
    light_palette["grid_color"] = "#e6e6e6"
    light_palette["text_color"] = "#2e2e2e"
    light_palette["dark_candle"] = "#4d98c4"
    light_palette["light_candle"] = "#b1b7ba"
    light_palette["volume_color"] = "#c74e96"
    light_palette["border_color"] = "#2e2e2e"
    light_palette["color_1"] = "#5c285b"
    light_palette["color_2"] = "#802c62"
    light_palette["color_3"] = "#a33262"
    light_palette["color_4"] = "#c43d5c"
    light_palette["color_5"] = "#de4f51"
    light_palette["color_6"] = "#f26841"
    light_palette["color_7"] = "#fd862b"
    light_palette["color_8"] = "#ffa600"
    light_palette["color_9"] = "#3366d6"
    palette = light_palette

    #  Create sub plots
    fig = make_subplots(rows=5, cols=1, subplot_titles=[f'Treasury yield'
                                                        , 'Unemployment Rate' \
                                                        , 'Savings', 'CPI' ,'PPI'] \
                        ,specs=[[{"secondary_y": True}], [{"secondary_y": True}] \
                         
                 
                               , [{"secondary_y": True}] \
                               , [{"secondary_y": True}] , [{"secondary_y": True}] ] \
                        ,vertical_spacing=0.1, shared_xaxes=True)

    #  Plots
    fig.add_trace(go.Scatter(x=treasury_yield_df.index, y=treasury_yield_df['DGS10']
                             , line=dict(color=palette['color_2']
                             , width=1), name="T-10"),row=1, col=1)
    fig.add_trace(go.Scatter(x=unemployment_rate_df.index, y=unemployment_rate_df['UNRATE']
                             , line=dict(color=palette['color_3'], width=1)
                             , name="Unemployement rate"),row=2, col=1)
    fig.add_trace(go.Scatter(x=savings_df.index, y=savings_df['PSAVERT']
                             , line=dict(color=palette['color_5'], width=1)
                             , name="Personal Savings Rate"),  row=3, col=1)
    fig.add_trace(go.Scatter(x=cpi_df.index, y=cpi_df['CPIAUCSL']
                             , line=dict(color=palette['color_6']
                             , width=1), name="CPI"), row=4, col=1)
    fig.add_trace(go.Scatter(x=ppi_df.index, y=ppi_df['PPIACO']
                             , line=dict(color=palette['color_7']
                             , width=1), name="PPI"), row=5, col=1)

    
    fig.update_layout(
            title={'text': '', 'x': 0.5},
            font=dict(family="Verdana", size=12, color=palette["text_color"]),
            autosize=True,
            width=1024, height=1024,
            xaxis={"rangeslider": {"visible": False}},
            plot_bgcolor=palette["plot_bg_color"],
            paper_bgcolor=palette["bg_color"])
    fig.update_layout(yaxis2=dict(range=[230, 300]))

    fig.update_xaxes(     dtick="M1",   tickformat="%b\n%Y",  ticklabelmode="period")

    #  Change grid color
    fig.update_xaxes(showline=True, linewidth=1
                     , linecolor=palette["grid_color"], gridcolor=palette["grid_color"])
    fig.update_yaxes(showline=True, linewidth=1
                     , linecolor=palette["grid_color"], gridcolor=palette["grid_color"])
    

    fig.show()

if __name__ == '__main__':

    #  Set start date
    start_date = dt.date(2018, 1, 1)

    start_date_str = dt.datetime.strftime(start_date, "%Y-%m-%d")
    #end = datetime.datetime(2023, 1, 1)
    
    #  Federal Reserve Economic Data Service
    data_source = 'fred'
    treasury_yield_code = 'DGS10' #  10-year Treasury Rate
    unemployment_rate_code = 'UNRATE'
    gdp_code = 'GDPC1'


    personal_savings_rate_code = 'PSAVERT'
    cpi_series = 'CPIAUCSL'
    ppi_series = 'PPIACO'
    
    #  Fetch data
    treasury_yield_df = pdr.DataReader(treasury_yield_code, data_source, start_date)
    unemployment_rate_df = pdr.DataReader(unemployment_rate_code, data_source, start_date)

    savings_df = pdr.DataReader(personal_savings_rate_code, data_source, start_date)
    cpi_df = pdr.DataReader(cpi_series, data_source, start_date)
    ppi_df = pdr.DataReader(ppi_series, data_source, start_date)  

    #  Plot charts
    plot_charts(treasury_yield_df, unemployment_rate_df ,savings_df, cpi_df ,ppi_df)
    #print('Done.')

def growth_rate(value,df):
    global initial_value
    if df[0] == value:
        initial_value = value
        return 0
    
    else:
        rate = f'{((value - initial_value) /initial_value) :.2f}'
        initial_value = value
        return float(rate) * 100

#retrieving the GDP figures
gdp = fred.get_series('GDPC1') #quarterly data
gdp.dropna(axis=0,inplace=True)
gdp_df = gdp.to_frame(name='gdp')
gdp_df['quarter'] = gdp_df.index
gdp_df.index = [index for index in  range(len(gdp_df))]

gdp_df['gdp_growth'] = gdp_df['gdp'].apply(growth_rate, df=gdp_df['gdp'])
gdp_df['quarter'] = pd.to_datetime(gdp_df['quarter'])
gdp_df['month'] = gdp_df['quarter'].dt.month

#gdp growth rate per year
gdp_year_df=gdp_df.loc[gdp_df['month']==10].copy()
gdp_year_df['year'] = gdp_year_df['quarter'].dt.year
gdp_year_df.reset_index(drop=True, inplace=True)
gdp_year_df.drop('month',axis=1,inplace=True)
gdp_year_df.drop('quarter',axis=1,inplace=True)

gdp_year_df['gdp_growth'] = gdp_year_df['gdp'].apply(growth_rate,df=gdp_year_df['gdp'])

#plotting gdp_growth_rate
plt.figure(figsize=(15,3))
plt.subplot(121)
plt.plot(gdp_df['quarter'] ,gdp_df['gdp_growth'],'r')
plt.title('Quarterly GDP Growth Rate of USA')
#---------------------------------------------------
plt.subplot(122)
plt.plot(gdp_year_df['year'],gdp_year_df['gdp_growth'],'g')
plt.title('Yearly GDP Growth Rate of USA')
#---------------------------------------------------

plt.show()

columns = ['AUS','JAP','USA','GBR','KOR']

aus = fred.get_series('NGDPRSAXDCAUQ')
jap = fred.get_series('JPNRGDPEXP')
us = fred.get_series('GDPC1')
uk = fred.get_series('NGDPRSAXDCGBQ')
korea = fred.get_series('NGDPRSAXDCKRQ')

#Combine all the single series, only take common dates using "inner"
gdp_global = pd.concat([aus, jap, us, uk, korea], join='inner', axis=1)

# Create YoY Real GDP (divided by 4 because we have quarterly data) & Drop NA's
gdp_global = ((gdp_global / gdp_global.shift(4)) - 1).dropna(axis = 0)
gdp_global.columns = columns

# Plot
plot1=gdp_global.hvplot(title = 'Global GDP', grid = True)
gdp_global.hvplot(title = 'Global GDP', grid = True)

plt.plot(gdp_global)
plt.title('Global GDP')
plt.show()

# Extract the 10Y Government Bond Yields for each country (make quarterly):
aus_y = fred.get_series('IRLTLT01AUM156N', frequency = 'q')
jap_y = fred.get_series('IRLTLT01JPM156N', frequency = 'q')
us_y = fred.get_series('IRLTLT01USM156N', frequency = 'q')
uk_y = fred.get_series('IRLTLT01GBM156N', frequency = 'q')
korea_y = fred.get_series('IRLTLT01KRM156N', frequency = 'q')

# Combine series
global_10y_yields = pd.concat([aus_y, jap_y, us_y, uk_y, korea_y], join='inner', axis=1)
global_10y_yields.columns = columns

# Plot
plot2=global_10y_yields.hvplot(title = 'Global 10Y Yields', grid = True)
global_10y_yields.hvplot(title = 'Global 10Y Yields', grid = True)

plt.plot(global_10y_yields)
plt.title('Global 10Y Yields')
plt.show()

df = pd.read_csv('tweetdata.csv')
df = df.drop(['Unnamed: 0','Date', 'User','likeCount','replyCount','retweetCount','location'],axis=1)
#df.isnull().sum()

def clean(text):
    text = str(text).lower()
    text = re.sub(r'https?:\/\/\S+', '', text)
    text = re.sub(r'[^a-zA-Z\s]', '', text)
    return text

df['Tweet'] = df['Tweet'].apply(clean)

#nltk.download('vader_lexicon')
for index, row in df['Tweet'].iteritems():
  score = SentimentIntensityAnalyzer().polarity_scores(row)
  neg = score['neg']
  neu = score['neu']
  pos = score['pos']
  if neg > pos:
    df.loc[index, 'Sentiment'] = 'Negative'
  elif pos > neg:
    df.loc[index, 'Sentiment'] = 'Positive'
  else:
    df.loc[index, 'Sentiment'] = 'Neutral'

C:\Users\user\AppData\Local\Temp\ipykernel_22720\4173848573.py:2: FutureWarning:

iteritems is deprecated and will be removed in a future version. Use .items instead.

def count_tp_in_column(data,feature):
  total = data.loc[:,feature].value_counts(dropna=False)
  percentage = data.loc[:,feature].value_counts(dropna=False, normalize=True)*100
  return pd.concat([total, round(percentage,2)], axis=1, keys=['Total', 'Percentage'])

tp = count_tp_in_column(df,'Sentiment')

labels = tp.index
sizes = tp['Percentage']
colors = ['#ff9999','#66b3ff','#99ff99']
 
fig1, ax1 = plt.subplots()
ax1.pie(sizes,colors=colors,labels=labels ,autopct='%1.1f%%' ,startangle=90)

centre_circle = plt.Circle((0,0),0.70,fc='white')
fig = plt.gcf()
fig.gca().add_artist(centre_circle)

ax1.axis('equal')  
plt.tight_layout()
plt.legend(title='Sentiment')

plt.show()

all_tweets = " ".join(tweet for tweet in df['Tweet'])
wordcloud = WordCloud(width=1024 ,height=512 ,random_state=21 ,max_font_size=110
                     ,background_color='white').generate(all_tweets)

plt.figure(figsize=(20, 10))
plt.imshow(wordcloud, interpolation="bilinear")
plt.axis('off')

plt.show()

#Import necessary libraries
import datetime
from datetime import date
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import yfinance as yf
import os

data = pd.read_csv('all_v2.csv')
data.head(3)

from pandas.core.base import value_counts
Building_type_column = 'building_type'
building_type_counts = data[Building_type_column].value_counts()

plt.figure(figsize=(10,6))
building_type_counts.plot(kind = 'bar', color = 'skyblue')
plt.xlabel('Building Type')
plt.ylabel('Count')
plt.title('Distribution of Building Types')

plt.tight_layout()
plt.show()

#csv_file_path = "C:\\Users\\EBEN\\Downloads\\GWP1\\all_v2.csv"
#data = pd.read_csv(csv_file_path)

def scatter_plots(df, colname_pairs, figscale=1, alpha=0.8):
    plt.figure(figsize=(len(colname_pairs) * 6 * figscale, 6 * figscale))
    
    for plot_i, (x_colname, y_colname) in enumerate(colname_pairs, start=1):
        ax = plt.subplot(1, len(colname_pairs), plot_i)
        df.plot(kind='scatter', x=x_colname, y=y_colname, s=(32 * figscale), alpha=alpha, ax=ax)
        ax.spines[['top', 'right']].set_visible(False)
    
    plt.tight_layout()
    plt.show()  

colname_pairs = [['price', 'geo_lat'], ['geo_lat', 'geo_lon'], ['geo_lon', 'region'], ['region', 'building_type']]

scatter_plots(data, colname_pairs)

def value_plot(data, y, figscale=1):
    data[y].plot(kind='line', figsize=(8 * figscale, 4 * figscale), title=y)
    plt.gca().spines[['top', 'right']].set_visible(False)
    plt.tight_layout()
    plt.title('Analysis of Price Trends in the Real Estate Market')

# Replace 'price' with the column name from your CSV file that you want to plot
chart = value_plot(data, 'price', figscale=1)
plt.show()

data['date'] = pd.to_datetime(data['date'])


plt.figure(figsize=(10, 6))
sns.kdeplot(data=data['date'], fill=True)  
plt.xlabel('Date')
plt.ylabel('Density')
plt.show()

	fiscalDateEnding	reportedEPS
0	2023-06-30	4.66
1	2022-09-30	6.11
2	2021-09-30	5.62
3	2020-09-30	3.27
4	2019-09-30	2.98
5	2018-09-30	2.97
6	2017-09-30	2.3
7	2016-09-30	2.0675
8	2015-09-30	2.3
9	2014-09-30	1.6075

	fiscalDateEnding	reportedCurrency	totalAssets	totalCurrentAssets	cashAndCashEquivalentsAtCarryingValue
0	2022-09-30	USD	352755000000	135405000000	23646000000
1	2021-09-30	USD	351002000000	134836000000	34940000000
2	2020-09-30	USD	323888000000	143713000000	38016000000
3	2019-09-30	USD	338516000000	162819000000	48844000000
4	2018-09-30	USD	365725000000	131339000000	25913000000

	fiscalDateEnding	reportedCurrency	operatingCashflow	paymentsForOperatingActivities	proceedsFromOperatingActivities
0	2022-09-30	USD	122151000000	4665000000	None
1	2021-09-30	USD	104038000000	4087000000	None
2	2020-09-30	USD	80674000000	4502000000	None
3	2019-09-30	USD	69391000000	3423000000	None
4	2018-09-30	USD	77434000000	3022000000	None

	fiscalDateEnding	reportedCurrency	grossProfit	totalRevenue	costOfRevenue
0	2022-09-30	USD	170782000000	391397000000	248640000000
1	2021-09-30	USD	152836000000	363172000000	234954000000
2	2020-09-30	USD	104956000000	271642000000	189475000000
3	2019-09-30	USD	98392000000	256598000000	180027000000
4	2018-09-30	USD	101839000000	265595000000	163756000000

	Open	High	Low	Close	Adj Close	Volume	company_name
Date
2023-09-14	145.080002	145.860001	142.949997	144.720001	144.720001	64033600	AMAZON
2023-09-15	142.690002	143.570007	140.089996	140.389999	140.389999	102861700	AMAZON
2023-09-18	140.479996	141.750000	139.220001	139.979996	139.979996	42823500	AMAZON
2023-09-19	138.699997	138.839996	135.559998	137.630005	137.630005	61482500	AMAZON
2023-09-20	138.550003	139.369995	135.199997	135.289993	135.289993	46263700	AMAZON
2023-09-21	131.940002	132.240005	129.309998	129.330002	129.330002	70234800	AMAZON
2023-09-22	131.110001	132.029999	128.520004	129.119995	129.119995	59859500	AMAZON
2023-09-25	129.360001	131.779999	128.770004	131.270004	131.270004	46017800	AMAZON
2023-09-26	130.119995	130.389999	125.279999	125.980003	125.980003	73048200	AMAZON
2023-09-27	125.760002	127.480003	124.129997	125.980003	125.980003	66496800	AMAZON

GWP1. Group Number 4175 Group Member : Yhasreen Ebenezer Oratile¶

Scenario 1¶

Scenario 2¶

Individual mortgage loan data¶

Macroeconomic factors¶

Housing Market Indicators¶

Scenario 3¶

Library for bond and equity¶

4. Equity Loan By Yhasreen¶

4a. Financial Statement¶

4b. Historical Prices vs Return¶

4c. Maroeconomic Factors¶

4d. Market Sentiment¶

5. Bond By Yhasreen¶

5a. Interest Rates and Inflation Data¶

5b. Economic Indicator¶

5c. Global Macroeconomic Factors¶

5d. Market Sentiment¶

Scenario 6¶

End of Project¶

	Open	High	Low	Close	Adj Close	Volume
count	251.000000	251.000000	251.000000	251.000000	251.000000	2.510000e+02
mean	161.597730	163.336056	160.025737	161.740717	161.320722	6.784617e+07
std	19.217619	18.968011	19.451388	19.173090	19.357984	2.279472e+07
min	126.010002	127.769997	124.169998	125.019997	124.488869	3.145820e+07
25%	145.815002	147.340004	144.014999	145.919998	145.274216	5.123105e+07
50%	159.940002	162.029999	159.350006	160.250000	159.812836	6.364660e+07
75%	177.799995	179.660004	176.665001	177.805000	177.655098	7.754820e+07
max	196.240005	198.229996	195.279999	196.449997	196.185074	1.647624e+08

	price	date	time	geo_lat	geo_lon	region	building_type	level	levels	rooms	area	kitchen_area	object_type
0	6050000	2018-02-19	20:00:21	59.805808	30.376141	2661	1	8	10	3	82.6	10.8	1
1	8650000	2018-02-27	12:04:54	55.683807	37.297405	81	3	5	24	2	69.1	12.0	1
2	4000000	2018-02-28	15:44:00	56.295250	44.061637	2871	1	5	9	3	66.0	10.0	1